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dc.contributor.authorSTATHAKIS DIMITRIOSen_GB
dc.contributor.authorKANELLOPOULOS IOANNISen_GB
dc.date.accessioned2010-02-25T14:38:49Z-
dc.date.available2008-03-12en_GB
dc.date.available2010-02-25T14:38:49Z-
dc.date.created2008-03-12en_GB
dc.date.issued2008en_GB
dc.date.submitted2006-09-07en_GB
dc.identifier.citationPHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING vol. 74 no. 1 p. 55-63en_GB
dc.identifier.issn0099-1112en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC34062-
dc.description.abstractThe development of digital global databases, containing data such as elevation and soil, can greatly simplify and aid in the classification of remotely sensed data to create land use classes. An efficient method that can simultaneously handle diverse input dimensions can be formed by merging fuzzy logic and neural networks. The so called granular or fuzzy neural networks are able not only to achieve high classification levels but at the same time produce compressed and transparent neural network skeletons. Compression results in reduced training times while transparency is an aid for interpreting the structure of the neural network by translating it into meaningful rules and vice versa. The purpose of this paper is to provide some initial guidelines for the construction of granular neural networks in the remote sensing context, while using global elevation ancillary data within the classification process.en_GB
dc.description.sponsorshipJRC.H.6-Spatial data infrastructuresen_GB
dc.format.mediumPrinteden_GB
dc.languageENGen_GB
dc.publisherAMER SOC PHOTOGRAMMETRYen_GB
dc.relation.ispartofseriesJRC34062en_GB
dc.titleGlobal Elevation Ancillary Data for Land Use Classification Using Granular Neural Networksen_GB
dc.typeArticles in periodicals and booksen_GB
JRC Directorate:Sustainable Resources

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